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                Deep Learning Hardwares And Drivers
              
            
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        <p><strong>说明：</strong>Deep Learning 服务器、显卡等硬件，以及CUDA，CUDNN介绍等。<br><a id="more"></a></p>
<p>参考文章：</p>
<hr>
<h2 id="服务器与工作站"><a href="#服务器与工作站" class="headerlink" title="服务器与工作站"></a>服务器与工作站</h2><h3 id="Amax服务器"><a href="#Amax服务器" class="headerlink" title="Amax服务器"></a>Amax服务器</h3><ul>
<li>苏州超集Amax公司。</li>
<li>2017年深度学习服务器更新DGX系列，DGC-1V要80万，据说相当于250台服务器，支持8卡最新的P100显卡。</li>
<li>工作站DevMax-400,401都支持4卡水冷，不到10万单价，满卡401需要99K。</li>
</ul>
<h3 id="Dell服务器"><a href="#Dell服务器" class="headerlink" title="Dell服务器"></a>Dell服务器</h3><ul>
<li>Dell PowerEdge系列：T630 塔式服务器，R720、R730 2U机架式服务器，但是720xd和730xd不支持GPU。</li>
</ul>
<hr>
<h2 id="GPU-显卡"><a href="#GPU-显卡" class="headerlink" title="GPU 显卡"></a>GPU 显卡</h2><h3 id="显卡分类与基本交火"><a href="#显卡分类与基本交火" class="headerlink" title="显卡分类与基本交火"></a>显卡分类与基本交火</h3><ul>
<li>显卡类型：集成显卡、核心显卡、独立显卡。总体上，独显优于核显优于集显，这三者功耗依次降低，但是性能对应下降。</li>
<li>混合交火和SLI<ul>
<li>CrossFire(CF)混合交火，是AMD公司旗下ATI公司（ATI Technologies Inc冶天科技）技术，后来免费授权，一般主板都支持CF。（A卡）</li>
<li>SLI可升级连接接口（Scalable Link Interface），是Nvidia公司技术。SLI需要收费授权，主板不一定支持SLI。增加一个显卡，性能提升不足单卡的80%，有的甚至不升反降，性价比比不上CrossFire。（N卡）</li>
<li>二者实际差不多，普通人都称为交火。交火最好是相同的显卡，或者同一厂家的同一系列产品。</li>
</ul>
</li>
</ul>
<h3 id="Nvidia-英伟达显卡"><a href="#Nvidia-英伟达显卡" class="headerlink" title="Nvidia 英伟达显卡"></a>Nvidia 英伟达显卡</h3><p>参考链接：</p>
<ul>
<li><a href="https://developer.nvidia.com/" target="_blank" rel="external">Nvidia developer官网</a></li>
<li><a href="http://docs.nvidia.com/cuda/index.html" target="_blank" rel="external">cuda文档API</a></li>
<li><a href="https://developer.nvidia.com/cuda-gpus" target="_blank" rel="external">cuda-gpus 列表</a></li>
<li><a href="https://en.wikipedia.org/wiki/CUDA#Limitations" target="_blank" rel="external">cuda wikipedia</a></li>
</ul>
<p>目前常用的NVIDIA独立显卡有Geforce，Quadro，Tesla 等系列的产品，都支持 NVIDIA CUDA并行计算平台。 NVIDIA GeForce（精视）和 NVIDIA Quadro 分别是为消费级图形处理和专业可视搜索化而设计的，Tesla 产品系列是完全针对并行计算而设计的，可提供独有的计算特性。由于Tesla系列产品的专业性，常用在地震处理, 信号与图像处理, 视频分析等对图形运算要求比较高的行业。</p>
<ul>
<li><strong>Geforce系列</strong> 考虑到性价比和性能需求，常用Geforce系列，Geforce包括GeForce GTX系列和NVIDIA TITAN系列。<ul>
<li><strong>GeForce GTX系列</strong>：比如GTX 970，GTX 980，GTX 1080，GTX1080 Ti等。早期产品基于Maxwell核心架构，新一代的核心架构是Pascal，当然Pascal架构的比Maxwell的高一代。</li>
<li><strong>NVIDIA TITAN系列</strong>：<ul>
<li>常见产品：早期的GeForce GTX TITAN（白泰坦）、GeForce GTX TITAN Black（黑泰坦）、GeForce GTX TITAN Z、GeForce GTX TITAN X。之后命名系列去掉GeForce GTX ，改为NVIDIA TITAN X、NVIDIA TITAN Xp等。Compute Capability参数指的是version，一般version越高，并行计算能力越强。</li>
<li>两个型号的Titan X？早期的 <code>NVIDIA GeForce GTX Titan X</code> 和后期的 <code>NVIDIA Titan X</code> 名称上都包含Titan X。但是早期的NVIDIA GeForce GTX Titan X基于上一代的Maxwell架构，NVIDIA Titan X基于新一代的Pascal架构。NVIDIA Titan X领先GTX 1080（基于Pascal） 30％，GTX 1080领先GTX Titan X 25％（来自百度知道）。</li>
<li>GTX Titan Z和GTX Titan X：GTX Titan Z 是内置的2路SLI，所以是单卡双核。gtx titan x是单卡单核。titan z早于 gtx titan x，titan z的功耗更大，性价比不是很高。多路SLI需要支持多路SLI的PCI-E Xx的主板或者服务器AMAX等，常见两路、三路、四路SLI，如一个电脑加四个gtx titan x构成四路SLI（四卡SLI），两个titan z构成两路SLI（两卡SLI），都能构成四核芯GPU。一般Nvidia最大支持4核芯，4卡SLI性能并不高，常用双卡SLI或者三卡SLI性能最高。</li>
</ul>
</li>
</ul>
</li>
<li><strong>Tesla系列</strong><ul>
<li>适用于专业领域，如Tesla K20/K40/K80等适合双精度要求高的场合，Tesla M40等适合单精度计算快，适合只训练深度学习网络。2017年有Tesla V100，比之前的M40、P100更强大。</li>
</ul>
</li>
</ul>
<h3 id="显卡参数"><a href="#显卡参数" class="headerlink" title="显卡参数"></a>显卡参数</h3><ul>
<li>核心和显存。核心是决定性的，显存对性能有辅助作用。</li>
<li>核心： 核心型号和核心参数。常见参数有：<ul>
<li>制作工艺：55nm，40纳米等，越小越先进，频率更高。</li>
<li>核心频率：700MHz等。</li>
<li>流处理器数量：48,800等，越多越强。不过，N卡和A卡架构不同，相同性能的两个核心，A卡的流处理器数量是N卡的4-5倍，所以两家不能比较流处理器数量。</li>
<li>流处理器频率：1400MHZ、700MHZ等等，频率越高，性能越强。不过，A卡和N卡架构有区别，N卡的流处理器频率一般是核心频率的两倍以上，而A卡的流处理器频率则与核心频率相同。</li>
</ul>
</li>
<li>显存：决定了数据大小和吞吐量，常见参数有：<ul>
<li>显存容量：256M，1G，12G等，决定能缓存的容量大小。</li>
<li>显存频率：DDR2、DDR3、GDDR3、GDDR5等几个类型，GDDR5的频率最高，等效频率能达到4GHZ以上。</li>
<li>显存位宽：64bit、256bit、512bit等几种。位宽越大，制造难度就越大，成本也越高，所以很多厂商宁可选择低位宽与高频率的组合，这样在保证性能的同时还能降低成本。</li>
<li>显存带宽=显存频率*显存位宽/8（B/s），表示GPU核心与显存之间数据传输速率。</li>
</ul>
</li>
<li>不同型号的核心，参数也不同，比如GT240，官方默认是96个流处理器、40NM工艺、550MHZ核心频率、1340MHZ流处理器频率、3400MHZ显存频率、128显存位宽。而实现产品中，按上面这些官方参数生产的显卡，称之为公版显卡，而达不到这些官方参数的显卡，我们就称之为缩水版显卡，而超出官方参数的显卡，我们就称之为超公版显卡。</li>
<li>显卡大小：有全长半长、全高半高之分，full/half length, full/half height，半高显卡也叫刀卡。GeForce系列以全长全高为主，如Nvidia高4.376’’(inch)，长10.5’’(inch)，高度约合11.1cm。Tesla有半高产品，支持2U(约8.89cm)机架式服务器，如<a href="http://www.nvidia.cn/object/where-to-buy-tesla-cn.html" target="_blank" rel="external">Tesla支持服务器</a>。机架式服务器Rack server(1U,2U等)，塔式服务器Tower server，刀片式服务器(高密度组装)。</li>
</ul>
<hr>
<h2 id="显卡配置-N卡为例"><a href="#显卡配置-N卡为例" class="headerlink" title="显卡配置(N卡为例)"></a>显卡配置(N卡为例)</h2><ul>
<li>参考文章<a href="http://blog.csdn.net/masa_fish/article/details/51882183" target="_blank" rel="external">Ubuntu 14.04 上安装 CUDA 7.5/8.0 超详细教程</a></li>
<li>配置深度学习环境的基本流程大致有以下几步，详细的安装建议查看<ul>
<li>安装NVIDIA显卡硬件，比如Nvidia Titan X</li>
<li>安装CUDA，全称CUDA(Compute Unified Device Architecture)，这是Nvidia公司为N卡开发的并行计算架构，这个一定要的。安装时主要参考<a href="http://docs.nvidia.com/cuda/" target="_blank" rel="external">官网的CUDA安装文档</a>，网上一大堆的教程说不定不行。</li>
<li>安装CUDNN，用于支持NN加速的工具包，不一定需要安装，网上也有一大堆教程，还是以<a href="https://developer.nvidia.com/cudnn" target="_blank" rel="external">官网的CUDNN说明</a>为主。</li>
<li>安装tf和pytorch等框架。</li>
</ul>
</li>
<li>配置使用<ul>
<li>运行<code>nvidia-smi -a</code> 查看N卡资源和运行状态</li>
<li>指定程序使用的显卡，几种方法：<ul>
<li>在环境变量（系统或者pycharm等IDE）中添加CUDA_VISIBLE_DEVICES=1或者CUDA_VISIBLE_DEVICES=0,2,3</li>
<li>程序中设置<code>import os os.environ[&quot;CUDA_DEVICE_ORDER&quot;]=&quot;PCI_BUS_ID&quot; os.environ[&quot;CUDA_VISIBLE_DEVICES&quot;]=&quot;0&quot;</code>等</li>
</ul>
</li>
</ul>
</li>
</ul>
<h3 id="安装cuda"><a href="#安装cuda" class="headerlink" title="安装cuda"></a>安装cuda</h3><ol>
<li>打开<a href="https://developer.nvidia.com/cuda-downloads?target_os=Linux&amp;target_arch=x86_64&amp;target_distro=Ubuntu&amp;target_version=1604&amp;target_type=deblocal" target="_blank" rel="external">cuda安装官网</a>，选择自己系统对应的版本，下载安装文件，如1.2G的deb文件。</li>
<li>在服务器上按照官网的命令进行安装，基本流程就是添加本地文件源，然后安装，命令如下。安装过程会默认在/usr/local中创建cuda链接，链接到cuda-8.0具体版本。</li>
</ol>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line">sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb</div><div class="line">sudo apt-key add /var/cuda-repo-&lt;version&gt;/7fa2af80.pub</div><div class="line">sudo apt-get update</div><div class="line">sudo apt-get install cuda</div></pre></td></tr></table></figure>
<ol>
<li>测试安装状态，运行nvidia-smi查看显卡状态，如果有错误提示Failed to initialize NVML: Driver/library version mismatch，可以尝试重启解决。</li>
</ol>
<h3 id="安装cudnn"><a href="#安装cudnn" class="headerlink" title="安装cudnn"></a>安装cudnn</h3><p>安装好cuda，可以进行nvidia-smi查看显卡状态，但是tf还需要使用cudnn加速，theano等可能就不一定需要。所以一般还需要再安装cudnn。</p>
<ol>
<li>打开<a href="https://developer.nvidia.com/rdp/cudnn-download" target="_blank" rel="external">cudnn下载官网</a>，需要注册邮箱账号，登录选择cudnn相应的版本，下载即可。</li>
<li>进行安装，比如deb包，使用<code>sudo dpkg -i xx.deb</code>即可。需要将安装后产生的libcudnn.so.6/7/8文件放到/usr/local/cuda/lib64下，如果找不到，可以全盘搜索文件<code>find / -name libcudnn*</code>，我的是被安装到<code>/usr/lib/x86_64-linux-gnu</code>，拷贝到/usr/local/cuda/lib64/下面并修改可执行权限，或者添加链接<code>sudo ln -s libcudnn.so.5.1 libcudnn.so</code></li>
<li>运行命令<code>nvcc</code>，需要确定libcudnn已经在path中，默认是不会添加到path的，不影响tf使用。如果要添加，可以修改.bashrc文件，添加</li>
</ol>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div></pre></td><td class="code"><pre><div class="line">export CUDNN_HOME=/usr/local/cuda</div><div class="line">export LD_LIBRARY_PATH=$&#123;CUDNN_HOME&#125;/lib64:$LD_LIBRARY_PATH</div><div class="line">export CPLUS_INCLUDE_PATH=$&#123;CUDNN_HOME&#125;/include:$CPLUS_INCLUDE_PATH</div></pre></td></tr></table></figure>
<ul>
<li>注意cuda、cudnn、tf、python之间的版本关系，三者需要相互支持。比如合适的搭配：cuda8、libcudnn6_cuda8、tf1.3.0、python3.6。</li>
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#服务器与工作站"><span class="nav-number">1.</span> <span class="nav-text">服务器与工作站</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#Amax服务器"><span class="nav-number">1.1.</span> <span class="nav-text">Amax服务器</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#Dell服务器"><span class="nav-number">1.2.</span> <span class="nav-text">Dell服务器</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#GPU-显卡"><span class="nav-number">2.</span> <span class="nav-text">GPU 显卡</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#显卡分类与基本交火"><span class="nav-number">2.1.</span> <span class="nav-text">显卡分类与基本交火</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#Nvidia-英伟达显卡"><span class="nav-number">2.2.</span> <span class="nav-text">Nvidia 英伟达显卡</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#显卡参数"><span class="nav-number">2.3.</span> <span class="nav-text">显卡参数</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#显卡配置-N卡为例"><span class="nav-number">3.</span> <span class="nav-text">显卡配置(N卡为例)</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#安装cuda"><span class="nav-number">3.1.</span> <span class="nav-text">安装cuda</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#安装cudnn"><span class="nav-number">3.2.</span> <span class="nav-text">安装cudnn</span></a></li></ol></li></ol></div>
            
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